In our method, we implement Transition function using Attention. Simply predicting next latent states independently using Attention can create inconsistencies between agents' current views of the environment, which can hinder performance. To solve this issue, we could theoretically predict next latent states autoregressively, that is, condition the latent state of \(i\) -th agent on the predicted latent states of the previous agents. However, this would significantly slow down training and would not allow decentralized decision making during execution. Another approach, proposed by [1]}, is to maximize mutual information between the latent state and the previous action of the agent:
\(\mathcal {L}_{\text{info}} = -I((h^i_t, z^i_{t}); a^i_{t-1})\) 
